[Answered] The increasing use of Artificial Intelligence (AI) in courtrooms requires clear guardrails. Examine the ethical and technical challenges in integrating AI into the justice system for ensuring fair, responsible, and transparent adjudication. (500 words)

Introduction

Artificial Intelligence can enhance efficiency in judicial processes, but without robust ethical and technical safeguards, it risks undermining fairness, accuracy, and public trust in the justice system.

Context and significance

1. India’s judiciary faces over 5 crore pending cases (National Judicial Data Grid, 2025). AI tools like transcription, translation, legal research, and defect identification promise efficiency.
2. Kerala High Court (2024) was the first to release guidelines for AI use; the eCourts Project Phase III envisions deeper digital integration.
3. Globally, AI pilots in courts include COMPAS risk assessment tools (U.S.), AI-supported sentencing (China), and predictive analytics (Estonia).

Policy Challenges

1. Bias and fairness: AI models learn from historical data, which may reflect societal or systemic biases (e.g., studies in the U.S. showed racial bias in COMPAS).
2. Hallucinations and misinformation: AI tools can create inaccurate translations or case citations (e.g., Supreme Court judge reported ‘leave granted’ translated as ‘holiday approved’).
3. Transparency and explainability: Most AI tools function as “black boxes.” Lack of explainability can erode litigant trust and make judicial review difficult.
4. Right to be informed: Litigants and lawyers must know when AI is used. There’s a need for consent and opt-out provisions in pilots.
5. Privacy and data security: Court records contain sensitive personal data; without strong protocols, risk of breaches and misuse rises.

Technical and institutional challenges

1. Infrastructure gaps: Majority of courts are still paper-based; digital divide and connectivity issues limit AI deployment in rural/district courts.
2. Quality of AI tools: Vendor solutions vary in accuracy; OpenAI’s Whisper and other LLMs can make errors or hallucinate content.
3. Procurement and oversight: Absence of standardised procurement and evaluation frameworks can lead to inappropriate adoption or vendor lock-in.
4. Capacity building: Judges, lawyers, and staff need AI literacy—not just usage training, but understanding limitations and risks. Judicial academies can collaborate with AI experts.
5. Data governance: Need policies for data ownership, anonymisation, and retention; absence of clear frameworks can undermine confidentiality.

Way forward – building guardrails

1. Policy frameworks: Formal guidelines like Kerala High Court’s policy should be expanded nationally; include ethical codes, performance metrics, and accountability mechanisms.
2. Human oversight: AI should remain an assistive tool, not a decision-maker; final adjudication must rest with judges.
3. Tech offices and audits: As suggested in eCourts Vision Document, set up technical cells for procurement, risk assessment, and periodic audits.
4. Stakeholder inclusion: Engage bar councils, industry, civil society in policy-making to ensure balance of efficiency and rights.
5. Global best practices: Adopt OECD AI Principles (transparency, accountability), EU’s AI Act approach to high-risk systems.

Conclusion

AI can modernise courts and reduce pendency, but must be guided by ethics, transparency, and human oversight. Responsible adoption ensures technology strengthens, not supplants, judicial reasoning and fairness.